The goal of this script is to generate a Seurat object for sample 2022_03.
LogNormalize, then doublets
detection using scran hybrid and scDblFinder
method, and doublet cells removalLogNormalize, for only the remaining
cellsPCAtSNE and UMAPlibrary(dplyr)
library(patchwork)
library(ggplot2)
.libPaths()
## [1] "/usr/local/lib/R/library"
In this section, we set the global settings of the analysis. We will store data there :
out_dir = "."
We load the parameters :
sample_name = params$sample_name # "2021_31"
# sample_name = "2021_31"
Input count matrix is there :
count_matrix_dir = paste0(out_dir, "/input/", sample_name)
We load the markers and specific colors for each cell type :
cell_markers = readRDS(paste0(out_dir, "/../1_metadata/hs_hd_cell_markers.rds"))
cell_markers = lapply(cell_markers, FUN = toupper)
lengths(cell_markers)
## T_CD4 T_CD8 mac_CPVL mac_TREM2 FC CO ME IBL
## 13 13 7 10 10 15 10 15
## IF mORS HFSC_1 HFSC_2 mela
## 22 14 17 18 10
Here are custom colors for each cell type :
color_markers = readRDS(paste0(out_dir, "/../1_metadata/hs_hd_color_markers.rds"))
data.frame(cell_type = names(color_markers),
color = unlist(color_markers)) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
ggplot2::geom_point(pch = 21, size = 5) +
ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position = "none",
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank())
We load markers to display on the dotplot :
dotplot_markers = readRDS(paste0(out_dir, "/../1_metadata/hs_hd_dotplot_markers.rds"))
dotplot_markers = lapply(dotplot_markers, FUN = toupper)
dotplot_markers
## $T_CD4
## [1] "CD3E" "CD4"
##
## $T_CD8
## [1] "CD3E" "CD8A"
##
## $mac_CPVL
## [1] "AIF1" "CPVL"
##
## $mac_TREM2
## [1] "AIF1" "TREM2"
##
## $FC
## [1] "CD3E" "CD8A"
##
## $CO
## [1] "KRT35" "KRT85"
##
## $ME
## [1] "BAMBI" "SLC7A8"
##
## $IBL
## [1] "KRT16" "KRT6B"
##
## $IF
## [1] "AQP3" "S100A8"
##
## $mORS
## [1] "IMPA2" "KRT6A"
##
## $HFSC_1
## [1] "DIO2" "TGFB2"
##
## $HFSC_2
## [1] "DIO2" "BHLHE41"
##
## $mela
## [1] "DCT" "MLANA"
We load metadata for this sample :
sample_info = readRDS(paste0(out_dir, "/../1_metadata/hs_hd_sample_info.rds"))
sample_info %>%
dplyr::filter(project_name == sample_name)
## project_name sample_type sample_identifier gender color
## 1 2022_03 HS HS_4 F #A84E37
These is a parameter for different functions :
cl = aquarius::create_parallel_instance(nthreads = 3L)
cut_log_nCount_RNA = 6
cut_nFeature_RNA = 500
cut_percent.mt = 20
cut_percent.rb = 50
In this section, we load the raw count matrix. Then, we applied an empty droplets filtering.
sobj = aquarius::load_sc_data(data_path = count_matrix_dir,
sample_name = sample_name,
my_seed = 1337L)
## [1] 27955 6794880
## [1] 47614257
## [1] 27955 5833
## [1] 43760300
## [1] 0.9190588
sobj
## An object of class Seurat
## 27955 features across 5833 samples within 1 assay
## Active assay: RNA (27955 features, 0 variable features)
(Time to run : 120.48 s)
In genes metadata, we add the Ensembl ID. The
sobj@assays$RNA@meta.features dataframe contains three
information :
rownames : gene names stored as the dimnames of the
count matrix. Duplicated gene names will have a .1 at the
end of their nameEnsembl_ID : EnsemblID, as stored in the
features.tsv.gz filegene_name : gene_name, as stored in the
features.tsv.gz file. Duplicated gene names will have the
same name.features_df = read.csv(paste0(count_matrix_dir, "/features.tsv.gz"), sep = "\t", header = 0)
features_df = features_df[, c(1:2)]
colnames(features_df) = c("Ensembl_ID", "gene_name")
rownames(features_df) = rownames(sobj) # mandatory for Seurat::FindVariableFeatures
sobj@assays$RNA@meta.features = features_df
rm(features_df)
head(sobj@assays$RNA@meta.features)
## Ensembl_ID gene_name
## MIR1302-2HG ENSG00000243485 MIR1302-2HG
## FAM138A ENSG00000237613 FAM138A
## OR4F5 ENSG00000186092 OR4F5
## AL627309.1 ENSG00000238009 AL627309.1
## AL627309.3 ENSG00000239945 AL627309.3
## AL627309.4 ENSG00000241599 AL627309.4
We add the same columns as in metadata :
row_oi = (sample_info$project_name == sample_name)
sobj$project_name = sample_name
sobj$sample_identifier = sample_info[row_oi, "sample_identifier"]
sobj$sample_type = sample_info[row_oi, "sample_type"]
colnames(sobj@meta.data)
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA"
## [4] "log_nCount_RNA" "project_name" "sample_identifier"
## [7] "sample_type"
sobj = Seurat::NormalizeData(sobj,
normalization.method = "LogNormalize",
assay = "RNA")
sobj = Seurat::FindVariableFeatures(sobj,
assay = "RNA",
nfeatures = 3000)
sobj
## An object of class Seurat
## 27955 features across 5833 samples within 1 assay
## Active assay: RNA (27955 features, 3000 variable features)
We generate a tSNE to visualize cells before filtering.
sobj = aquarius::dimensions_reduction(sobj = sobj,
assay = "RNA",
reduction = "pca",
max_dims = 100,
verbose = FALSE)
Seurat::ElbowPlot(sobj, ndims = 100, reduction = "RNA_pca")
We generate a tSNE with 20 principal components :
ndims = 20
sobj = Seurat::RunTSNE(sobj,
reduction = "RNA_pca",
dims = 1:ndims,
seed.use = 1337L,
reduction.name = paste0("RNA_pca_", ndims, "_tsne"))
sobj
## An object of class Seurat
## 27955 features across 5833 samples within 1 assay
## Active assay: RNA (27955 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
We annotate cells for cell type using
Seurat::AddModuleScore function.
sobj = aquarius::cell_annot_custom(sobj,
newname = "cell_type",
markers = cell_markers,
use_negative = TRUE,
add_score = TRUE,
verbose = TRUE)
colnames(sobj@meta.data) = stringr::str_replace_all(string = colnames(sobj@meta.data),
pattern = " ",
replacement = "_")
sobj$cell_type = factor(sobj$cell_type, levels = names(cell_markers))
table(sobj$cell_type)
##
## T_CD4 T_CD8 mac_CPVL mac_TREM2 FC CO ME IBL
## 496 342 96 157 323 491 306 258
## IF mORS HFSC_1 HFSC_2 mela
## 1462 445 355 157 945
(Time to run : 27.29 s)
To justify cell type annotation, we can make a dotplot :
markers = c("PTPRC", "MSX2", "KRT16",
unique(unlist(dotplot_markers[levels(sobj$cell_type)])))
markers = markers[markers %in% rownames(sobj)]
aquarius::plot_dotplot(sobj, assay = "RNA",
column_name = "cell_type",
markers = markers,
nb_hline = 0) +
ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
ggplot2::theme(legend.position = "right",
legend.box = "vertical",
legend.direction = "vertical",
axis.title = element_blank(),
axis.text = element_text(size = 15))
We can make a barplot to see the composition of each dataset, and visualize cell types on the projection.
df_proportion = as.data.frame(prop.table(table(sobj$orig.ident,
sobj$cell_type)))
colnames(df_proportion) = c("orig.ident", "cell_type", "freq")
quantif = table(sobj$orig.ident) %>%
as.data.frame.table() %>%
`colnames<-`(c("orig.ident", "nb_cells"))
# Plot
plot_list = list()
plot_list[[2]] = aquarius::plot_barplot(df = df_proportion,
x = "orig.ident",
y = "freq",
fill = "cell_type",
position = ggplot2::position_fill()) +
ggplot2::scale_fill_manual(name = "Cell type",
values = color_markers[levels(df_proportion$cell_type)],
breaks = levels(df_proportion$cell_type)) +
ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
aes(x = orig.ident, y = 1.05, label = nb_cells),
label.size = 0)
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "cell_type") +
ggplot2::scale_color_manual(values = unlist(color_markers),
breaks = names(color_markers)) +
ggplot2::labs(title = sample_name,
subtitle = paste0(ncol(sobj), " cells")) +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
patchwork::wrap_plots(plot_list, nrow = 1, widths = c(6, 1))
We annotate cells for cell cycle phase using Seurat and
cyclone.
cc_columns = aquarius::add_cell_cycle(sobj = sobj,
assay = "RNA",
species_rdx = "hs",
BPPARAM = cl)@meta.data[, c("Seurat.Phase", "Phase")]
##
## G1 G2M S
## 2708 903 2220
sobj$Seurat.Phase = cc_columns$Seurat.Phase
sobj$cyclone.Phase = cc_columns$Phase
table(sobj$Seurat.Phase, sobj$cyclone.Phase)
##
## G1 G2M S
## G1 1828 490 1518
## G2M 269 297 133
## S 611 116 569
(Time to run : 529 s)
We visualize cell cycle on the projection :
plot_list = list()
plot_list[[2]] = Seurat::DimPlot(sobj, group.by = "Seurat.Phase") +
ggplot2::labs(title = "Cell Cycle Phase",
subtitle = "Seurat.Phase") +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "cyclone.Phase") +
ggplot2::labs(title = "Cell Cycle Phase",
subtitle = "cyclone.Phase") +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
patchwork::wrap_plots(plot_list, nrow = 1)
In this section, we look at the number of genes expressed by each cell, the number of UMI, the percentage of mitochondrial genes expressed, and the percentage of ribosomal genes expressed. Then, without taking into account the cells expressing low number of genes or have low number of UMI, we identify doublet cells.
We compute four quality metrics :
sobj = Seurat::PercentageFeatureSet(sobj, pattern = "^MT", col.name = "percent.mt")
sobj = Seurat::PercentageFeatureSet(sobj, pattern = "^RP[L|S][0-9]*$", col.name = "percent.rb")
head(sobj@meta.data)
## orig.ident nCount_RNA nFeature_RNA log_nCount_RNA
## AAACCCAAGACGGTCA-1 2022_03 31319 5679 10.351980
## AAACCCAAGAGCTGCA-1 2022_03 11008 3072 9.306378
## AAACCCAAGAGGGTCT-1 2022_03 421 312 6.042633
## AAACCCAAGCATGTTC-1 2022_03 13043 3576 9.476007
## AAACCCACATCACCAA-1 2022_03 606 343 6.406880
## AAACCCAGTCGTCATA-1 2022_03 2508 886 7.827241
## project_name sample_identifier sample_type score_T_CD4
## AAACCCAAGACGGTCA-1 2022_03 HS_4 HS -0.4541055
## AAACCCAAGAGCTGCA-1 2022_03 HS_4 HS -0.3004685
## AAACCCAAGAGGGTCT-1 2022_03 HS_4 HS -0.1294684
## AAACCCAAGCATGTTC-1 2022_03 HS_4 HS -0.3478487
## AAACCCACATCACCAA-1 2022_03 HS_4 HS 0.3811207
## AAACCCAGTCGTCATA-1 2022_03 HS_4 HS 0.2243846
## score_T_CD8 score_mac_CPVL score_mac_TREM2 score_FC
## AAACCCAAGACGGTCA-1 -0.40653698 -0.32946830 -0.09814741 -0.372758734
## AAACCCAAGAGCTGCA-1 -0.23349365 -0.09122777 0.04926414 -0.499070984
## AAACCCAAGAGGGTCT-1 -0.11782654 -0.06651616 -0.01368420 -0.164863137
## AAACCCAAGCATGTTC-1 -0.32548351 -0.27379849 -0.07446076 -0.004041001
## AAACCCACATCACCAA-1 0.12535778 -0.06295197 -0.03356636 0.168125119
## AAACCCAGTCGTCATA-1 0.09272761 2.82077708 0.57110232 -0.132580540
## score_CO score_ME score_IBL score_IF score_mORS
## AAACCCAAGACGGTCA-1 0.9797889 0.27313104 -0.1272997 -0.70037687 -0.58582496
## AAACCCAAGAGCTGCA-1 0.5340101 1.71923447 -0.2102346 -0.49567109 -0.45897772
## AAACCCAAGAGGGTCT-1 0.1473473 -0.06256291 0.1055160 -0.25392042 -0.20113388
## AAACCCAAGCATGTTC-1 -0.0294312 -0.22668836 -0.2207497 0.52144353 0.04373350
## AAACCCACATCACCAA-1 0.1027826 -0.05516201 -0.0910277 -0.05973959 0.05982404
## AAACCCAGTCGTCATA-1 -0.1598181 -0.10933236 -0.1023399 -0.39348951 -0.23252143
## score_HFSC_1 score_HFSC_2 score_mela cell_type Seurat.Phase
## AAACCCAAGACGGTCA-1 -0.23895507 -0.18765847 -0.6915493 CO G2M
## AAACCCAAGAGCTGCA-1 -0.07856441 0.02069982 -0.3704869 ME G1
## AAACCCAAGAGGGTCT-1 -0.08170850 -0.06846754 1.3523089 mela G2M
## AAACCCAAGCATGTTC-1 0.03501438 -0.16559145 -0.5316708 IF G1
## AAACCCACATCACCAA-1 -0.06341535 -0.05732026 -0.1546279 T_CD4 G1
## AAACCCAGTCGTCATA-1 0.09012891 -0.04175896 -0.3670305 mac_CPVL G1
## cyclone.Phase percent.mt percent.rb
## AAACCCAAGACGGTCA-1 G2M 5.2428238 17.273859
## AAACCCAAGAGCTGCA-1 S 6.8223110 23.210392
## AAACCCAAGAGGGTCT-1 S 10.9263658 14.964371
## AAACCCAAGCATGTTC-1 S 5.3438626 26.059956
## AAACCCACATCACCAA-1 G1 20.7920792 3.465347
## AAACCCAGTCGTCATA-1 G1 0.3189793 27.073365
We get the cell barcodes for the failing cells :
fail_percent.mt = sobj@meta.data %>% dplyr::filter(percent.mt > cut_percent.mt) %>% rownames()
fail_percent.rb = sobj@meta.data %>% dplyr::filter(percent.rb > cut_percent.rb) %>% rownames()
fail_log_nCount_RNA = sobj@meta.data %>% dplyr::filter(log_nCount_RNA < cut_log_nCount_RNA) %>% rownames()
fail_nFeature_RNA = sobj@meta.data %>% dplyr::filter(nFeature_RNA < cut_nFeature_RNA) %>% rownames()
Without taking into account the low UMI and low number of features cells, we identify doublets.
fsobj = subset(sobj, invert = TRUE,
cells = unique(c(fail_log_nCount_RNA, fail_nFeature_RNA)))
fsobj
## An object of class Seurat
## 27955 features across 4875 samples within 1 assay
## Active assay: RNA (27955 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
On this filtered dataset, we apply doublet cells detection. Just before, we run the normalization, taking into account only the remaining cells.
sobj = Seurat::NormalizeData(sobj,
normalization.method = "LogNormalize",
assay = "RNA")
sobj = Seurat::FindVariableFeatures(sobj,
assay = "RNA",
nfeatures = 3000)
sobj
## An object of class Seurat
## 27955 features across 5833 samples within 1 assay
## Active assay: RNA (27955 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
We identify doublet cells :
fsobj = aquarius::find_doublets(sobj = fsobj,
BPPARAM = cl)
## [1] 27955 4875
##
## FALSE TRUE
## 4434 441
## [19:27:23] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
##
## FALSE TRUE
## 4470 405
##
## FALSE TRUE
## 4192 683
fail_doublets_consensus = Seurat::WhichCells(fsobj, expression = doublets_consensus.class)
fail_doublets_scDblFinder = Seurat::WhichCells(fsobj, expression = scDblFinder.class)
fail_doublets_hybrid = Seurat::WhichCells(fsobj, expression = hybrid_score.class)
(Time to run : 127.82 s)
We add the information in the non filtered Seurat object :
sobj$doublets_consensus.class = dplyr::case_when(!(colnames(sobj) %in% colnames(fsobj)) ~ NA,
colnames(sobj) %in% fail_doublets_consensus ~ TRUE,
!(colnames(sobj) %in% fail_doublets_consensus) ~ FALSE)
sobj$scDblFinder.class = dplyr::case_when(!(colnames(sobj) %in% colnames(fsobj)) ~ NA,
colnames(sobj) %in% fail_doublets_scDblFinder ~ TRUE,
!(colnames(sobj) %in% fail_doublets_scDblFinder) ~ FALSE)
sobj$hybrid_score.class = dplyr::case_when(!(colnames(sobj) %in% colnames(fsobj)) ~ NA,
colnames(sobj) %in% fail_doublets_hybrid ~ TRUE,
!(colnames(sobj) %in% fail_doublets_hybrid) ~ FALSE)
We can visualize the 4 cells quality with a Venn diagram :
n_filtered = c(fail_percent.mt, fail_percent.rb, fail_log_nCount_RNA, fail_nFeature_RNA) %>%
unique() %>% length()
percent_filtered = round(100*(n_filtered/ncol(sobj)), 2)
ggvenn::ggvenn(list(percent.mt = fail_percent.mt,
percent.rb = fail_percent.rb,
log_nCount_RNA = fail_log_nCount_RNA,
nFeature_RNA = fail_nFeature_RNA),
fill_color = c("#0073C2FF", "#EFC000FF", "orange", "pink"),
stroke_size = 0.5, set_name_size = 4) +
ggplot2::labs(title = "Filtered out cells",
subtitle = paste0(n_filtered, " cells (", percent_filtered, " % of all cells)")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
To visualize the threshold for number of UMI, we can make a histogram :
aquarius::plot_qc_density(df = sobj@meta.data,
x = "log_nCount_RNA",
bins = 200,
group_by = "orig.ident",
group_color = setNames(sample_info$color,
nm = sample_info$sample_identifiant),
x_thresh = cut_log_nCount_RNA)
Seurat::VlnPlot(sobj, features = "log_nCount_RNA", pt.size = 0.001,
group.by = "cell_type", cols = color_markers) +
ggplot2::scale_fill_manual(values = color_markers, breaks = names(color_markers)) +
ggplot2::geom_hline(yintercept = cut_log_nCount_RNA, col = "red") +
ggplot2::labs(x = "")
sobj$fail = ifelse(colnames(sobj) %in% fail_log_nCount_RNA,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
Seurat::DimPlot(sobj, group.by = "fail", na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "log_nCount_RNA",
subtitle = paste0(length(fail_log_nCount_RNA), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
To visualize the threshold for number of features, we can make a histogram :
aquarius::plot_qc_density(df = sobj@meta.data,
x = "nFeature_RNA",
bins = 200,
group_by = "orig.ident",
group_color = setNames(sample_info$color,
nm = sample_info$sample_identifiant),
x_thresh = cut_nFeature_RNA)
Seurat::VlnPlot(sobj, features = "nFeature_RNA", pt.size = 0.001,
group.by = "cell_type", cols = color_markers) +
ggplot2::scale_fill_manual(values = color_markers, breaks = names(color_markers)) +
ggplot2::geom_hline(yintercept = cut_nFeature_RNA, col = "red") +
ggplot2::labs(x = "")
sobj$fail = ifelse(colnames(sobj) %in% fail_nFeature_RNA,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
Seurat::DimPlot(sobj, group.by = "fail", na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "nFeature_RNA",
subtitle = paste0(length(fail_nFeature_RNA), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
To identify a threshold for mitochondrial gene expression, we can make a histogram :
aquarius::plot_qc_density(df = sobj@meta.data,
x = "percent.mt",
bins = 200,
group_by = "orig.ident",
group_color = setNames(sample_info$color,
nm = sample_info$sample_identifiant),
x_thresh = cut_percent.mt)
Seurat::VlnPlot(sobj, features = "percent.mt", pt.size = 0.001,
group.by = "cell_type", cols = color_markers) +
ggplot2::scale_fill_manual(values = color_markers, breaks = names(color_markers)) +
ggplot2::geom_hline(yintercept = cut_percent.mt, col = "red") +
ggplot2::labs(x = "")
sobj$fail = ifelse(colnames(sobj) %in% fail_percent.mt,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
Seurat::DimPlot(sobj, group.by = "fail", na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "percent.mt",
subtitle = paste0(length(fail_percent.mt), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
To identify a threshold for ribosomal gene expression, we can make a histogram :
aquarius::plot_qc_density(df = sobj@meta.data,
x = "percent.rb",
bins = 200,
group_by = "orig.ident",
group_color = setNames(sample_info$color,
nm = sample_info$sample_identifiant),
x_thresh = cut_percent.rb)
Seurat::VlnPlot(sobj, features = "percent.rb", pt.size = 0.001,
group.by = "cell_type", cols = color_markers) +
ggplot2::scale_fill_manual(values = color_markers, breaks = names(color_markers)) +
ggplot2::geom_hline(yintercept = cut_percent.rb, col = "red") +
ggplot2::labs(x = "")
sobj$fail = ifelse(colnames(sobj) %in% fail_percent.rb,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
Seurat::DimPlot(sobj, group.by = "fail", na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "percent.rb",
subtitle = paste0(length(fail_percent.rb), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
We would like to see if the number of feature expressed by cell, and
the number of UMI is correlated with the cell type, the percentage of
mitochondrial and ribosomal gene expressed, and the doublet status. We
build the log_nCount_RNA by nFeature_RNA
figure, where cells (dots) are colored by these different metrics.
This is the figure, colored by cell type :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "cell_type",
col_colors = unname(color_markers),
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the percentage of mitochondrial genes expressed in cell :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "percent.mt",
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the percentage of ribosomal genes expressed in cell :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "percent.rb",
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the doublet cells status
(doublets_consensus.class) :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "doublets_consensus.class",
col_colors = setNames(nm = c(TRUE, FALSE),
aquarius::gg_color_hue(2)),
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the doublet cells status
(scDblFinder.class) :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "scDblFinder.class",
col_colors = setNames(nm = c(TRUE, FALSE),
aquarius::gg_color_hue(2)),
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the doublet cells status
(hybrid_score.class) :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "hybrid_score.class",
col_colors = setNames(nm = c(TRUE, FALSE),
aquarius::gg_color_hue(2)),
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
Do filtered cells belong to a particular cell type ?
sobj$all_cells = TRUE
plot_list = list()
## All cells
df = sobj@meta.data
if (nrow(df) == 0) {
plot_list[[1]] = ggplot()
} else {
plot_list[[1]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = "All cells",
subtitle = paste(nrow(df), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## Doublets consensus
df = sobj@meta.data %>%
dplyr::filter(doublets_consensus.class)
if (nrow(df) == 0) {
plot_list[[2]] = ggplot()
} else {
plot_list[[2]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = "doublets_consensus.class",
subtitle = paste(sum(sobj$doublets_consensus.class, na.rm = TRUE), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## percent.mt
df = sobj@meta.data %>%
dplyr::filter(percent.mt > cut_percent.mt)
if (nrow(df) == 0) {
plot_list[[3]] = ggplot()
} else {
plot_list[[3]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = paste("percent.mt >", cut_percent.mt),
subtitle = paste(length(fail_percent.mt), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## percent.rb
df = sobj@meta.data %>%
dplyr::filter(percent.rb > cut_percent.rb)
if (nrow(df) == 0) {
plot_list[[4]] = ggplot()
} else {
plot_list[[4]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = paste("percent.rb >", cut_percent.rb),
subtitle = paste(length(fail_percent.rb), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## log_nCount_RNA
df = sobj@meta.data %>%
dplyr::filter(log_nCount_RNA < cut_log_nCount_RNA)
if (nrow(df) == 0) {
plot_list[[5]] = ggplot()
} else {
plot_list[[5]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = paste("log_nCount_RNA <", round(cut_log_nCount_RNA, 2)),
subtitle = paste(length(fail_log_nCount_RNA), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## nFeature_RNA
df = sobj@meta.data %>%
dplyr::filter(nFeature_RNA < cut_nFeature_RNA)
if (nrow(df) == 0) {
plot_list[[6]] = ggplot()
} else {
plot_list[[6]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = paste("nFeature_RNA <", round(cut_nFeature_RNA, 2)),
subtitle = paste(length(fail_nFeature_RNA), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
patchwork::wrap_plots(plot_list, ncol = 3) +
patchwork::plot_layout(guides = "collect") &
ggplot2::theme(legend.position = "right")
We can compare doublet detection methods with a Venn diagram :
ggvenn::ggvenn(list(hybrid = fail_doublets_hybrid,
scDblFinder = fail_doublets_scDblFinder),
fill_color = c("#0073C2FF", "#EFC000FF"),
stroke_size = 0.5, set_name_size = 4) +
ggplot2::ggtitle(label = "Doublet cells") +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"))
We visualize cells annotation for doublets :
plot_list = list()
# scDblFinder.class
sobj$fail = ifelse(sobj$scDblFinder.class,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "fail",
na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "scDblFinder.class",
subtitle = paste0(sum(sobj$scDblFinder.class, na.rm = TRUE), " cells")) +
Seurat::NoAxes() + Seurat::NoLegend() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
# hybrid_score.class
sobj$fail = ifelse(sobj$hybrid_score.class,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
plot_list[[2]] = Seurat::DimPlot(sobj, group.by = "fail",
na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "hybrid_score.class",
subtitle = paste0(sum(sobj$hybrid_score.class, na.rm = TRUE), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
sobj$fail = NULL
# Plot
patchwork::wrap_plots(plot_list, nrow = 1)
What is the composition of doublet cells ? We just look at score for each cell type.
sobj$orig.ident.doublets = case_when(is.na(sobj$doublets_consensus.class) ~ "bad quality",
sobj$doublets_consensus.class == TRUE ~ paste0(sobj$orig.ident, " doublets"),
sobj$doublets_consensus.class == FALSE ~ "not doublet")
sobj$orig.ident.doublets = factor(sobj$orig.ident.doublets,
levels = c(paste0(as.character(sample_info$sample_identifiant), " doublets"),
"bad quality", "not doublet"))
doublets_compo = function(score1, score2) {
type1 = unlist(lapply(stringr::str_split(score1, pattern = "score_"), `[[`, 2))
type2 = unlist(lapply(stringr::str_split(score2, pattern = "score_"), `[[`, 2))
if (type1 == type2) {
the_title = "Homotypic doublet"
the_subtitle = type1
score1 = "log_nCount_RNA"
} else {
the_title = "Heterotypic doublet"
the_subtitle = paste(type1, type2, sep = " + ")
}
p = sobj@meta.data %>%
dplyr::arrange(desc(orig.ident.doublets)) %>%
ggplot2::ggplot(., aes(x = eval(parse(text = score1)),
y = eval(parse(text = score2)),
col = orig.ident.doublets)) +
ggplot2::geom_point(size = 0.25) +
ggplot2::scale_color_manual(values = c(sample_info$color, "gray90", "gray60"),
breaks = c(paste0(as.character(sample_info$sample_identifiant), " doublets"),
"bad quality", "not doublet")) +
ggplot2::labs(x = score1, y = score2,
title = the_title, subtitle = the_subtitle) +
ggplot2::theme_classic() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
return(p)
}
score_columns = grep(x = colnames(sobj@meta.data),
pattern = "^score",
value = TRUE)
combinations = expand.grid(score_columns, score_columns) %>%
apply(., 1, sort) %>% t() %>%
as.data.frame()
combinations = combinations[!duplicated(combinations), ]
plot_list = apply(combinations, 1, FUN = function(elem) {
doublets_compo(elem[1], elem[2])
})
sobj$orig.ident.doublets = NULL
patchwork::wrap_plots(plot_list, ncol = 4) +
patchwork::plot_layout(guides = "collect") &
ggplot2::theme(legend.position = "right")
We could save this object before filtering (remove
eval = FALSE) :
saveRDS(sobj, paste0(out_dir, "/datasets/", sample_name, "_sobj_unfiltered.rds"))
We remove :
sobj = subset(sobj, invert = TRUE,
cells = unique(c(fail_log_nCount_RNA, fail_nFeature_RNA,
fail_percent.mt, fail_percent.rb,
fail_doublets_consensus)))
sobj
## An object of class Seurat
## 27955 features across 3977 samples within 1 assay
## Active assay: RNA (27955 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
We normalize the count matrix for remaining cells :
sobj = Seurat::NormalizeData(sobj,
normalization.method = "LogNormalize",
assay = "RNA")
sobj = Seurat::FindVariableFeatures(sobj,
assay = "RNA",
nfeatures = 3000)
sobj
## An object of class Seurat
## 27955 features across 3977 samples within 1 assay
## Active assay: RNA (27955 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
We perform a PCA :
sobj = aquarius::dimensions_reduction(sobj = sobj,
assay = "RNA",
reduction = "pca",
max_dims = 100,
verbose = FALSE)
Seurat::ElbowPlot(sobj, ndims = 100, reduction = "RNA_pca")
We generate a tSNE and a UMAP with 20 principal components :
ndims = 20
sobj = Seurat::RunTSNE(sobj,
reduction = "RNA_pca",
dims = 1:ndims,
seed.use = 1337L,
reduction.name = paste0("RNA_pca_", ndims, "_tsne"))
sobj = Seurat::RunUMAP(sobj,
reduction = "RNA_pca",
dims = 1:ndims,
seed.use = 1337L,
reduction.name = paste0("RNA_pca_", ndims, "_umap"))
We annotate cells for cell type, with the new normalized expression matrix :
score_columns = grep(x = colnames(sobj@meta.data), pattern = "^score", value = TRUE)
sobj@meta.data[, score_columns] = NULL
sobj$cell_type = NULL
sobj = aquarius::cell_annot_custom(sobj,
newname = "cell_type",
markers = cell_markers,
use_negative = TRUE,
add_score = TRUE,
verbose = TRUE)
sobj$cell_type = factor(sobj$cell_type, levels = names(cell_markers))
colnames(sobj@meta.data) = stringr::str_replace_all(string = colnames(sobj@meta.data),
pattern = " ",
replacement = "_")
table(sobj$cell_type)
##
## T_CD4 T_CD8 mac_CPVL mac_TREM2 FC CO ME IBL
## 448 316 55 93 192 382 210 137
## IF mORS HFSC_1 HFSC_2 mela
## 1034 222 269 97 522
(Time to run : 18.73 s)
To justify cell type annotation, we can make a dotplot :
markers = c("PTPRC", unique(unlist(dotplot_markers[levels(sobj$cell_type)])))
markers = markers[markers %in% rownames(sobj)]
aquarius::plot_dotplot(sobj, assay = "RNA",
column_name = "cell_type",
markers = markers,
nb_hline = 0) +
ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
ggplot2::theme(legend.position = "right",
legend.box = "vertical",
legend.direction = "vertical",
axis.title = element_blank(),
axis.text = element_text(size = 15))
We can make a barplot to see the composition of each dataset, and visualize cell types on the projection.
df_proportion = as.data.frame(prop.table(table(sobj$orig.ident,
sobj$cell_type)))
colnames(df_proportion) = c("orig.ident", "cell_type", "freq")
quantif = table(sobj$orig.ident) %>%
as.data.frame.table() %>%
`colnames<-`(c("orig.ident", "nb_cells"))
# Plot
plot_list = list()
plot_list[[2]] = aquarius::plot_barplot(df = df_proportion,
x = "orig.ident",
y = "freq",
fill = "cell_type",
position = ggplot2::position_fill()) +
ggplot2::scale_fill_manual(name = "Cell type",
values = color_markers[levels(df_proportion$cell_type)],
breaks = levels(df_proportion$cell_type)) +
ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
aes(x = orig.ident, y = 1.05, label = nb_cells),
label.size = 0)
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "cell_type",
reduction = "RNA_pca_20_tsne") +
ggplot2::scale_color_manual(values = unlist(color_markers),
breaks = names(color_markers)) +
ggplot2::labs(title = sample_name,
subtitle = paste0(ncol(sobj), " cells")) +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
patchwork::wrap_plots(plot_list, nrow = 1, widths = c(6, 1))
We annotate cells for cell cycle phase :
cc_columns = aquarius::add_cell_cycle(sobj = sobj,
assay = "RNA",
species_rdx = "hs",
BPPARAM = cl)@meta.data[, c("Seurat.Phase", "Phase")]
##
## G1 G2M S
## 2025 426 1526
sobj$Seurat.Phase = cc_columns$Seurat.Phase
sobj$cyclone.Phase = cc_columns$Phase
table(sobj$Seurat.Phase, sobj$cyclone.Phase)
##
## G1 G2M S
## G1 1386 245 1068
## G2M 176 134 83
## S 463 47 375
(Time to run : 396.04 s)
We visualize cell cycle on the projection :
plot_list = list()
plot_list[[2]] = Seurat::DimPlot(sobj, group.by = "Seurat.Phase",
reduction = "RNA_pca_20_tsne") +
ggplot2::labs(title = "Cell Cycle Phase",
subtitle = "Seurat.Phase") +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "cyclone.Phase",
reduction = "RNA_pca_20_tsne") +
ggplot2::labs(title = "Cell Cycle Phase",
subtitle = "cyclone.Phase") +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
patchwork::wrap_plots(plot_list, nrow = 1)
We make a highly resolutive clustering :
sobj = Seurat::FindNeighbors(sobj, reduction = "RNA_pca", dims = c(1:ndims))
sobj = Seurat::FindClusters(sobj, resolution = 2)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 3977
## Number of edges: 132933
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8222
## Number of communities: 25
## Elapsed time: 0 seconds
table(sobj$seurat_clusters)
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## 384 320 298 289 278 268 183 180 175 156 156 152 139 139 136 96 95 93 76 76
## 20 21 22 23 24
## 73 70 58 45 42
We can visualize the cell type :
tsne = Seurat::DimPlot(sobj, group.by = "cell_type",
reduction = paste0("RNA_pca_", ndims, "_tsne"), cols = color_markers) +
Seurat::NoAxes() + ggplot2::ggtitle("tSNE") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
legend.position = "none")
umap = Seurat::DimPlot(sobj, group.by = "cell_type",
reduction = paste0("RNA_pca_", ndims, "_umap"), cols = color_markers) +
Seurat::NoAxes() + ggplot2::ggtitle("UMAP") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
tsne | umap
We can visualize the cell cycle, from Seurat :
tsne = Seurat::DimPlot(sobj, group.by = "Seurat.Phase",
reduction = paste0("RNA_pca_", ndims, "_tsne")) +
Seurat::NoAxes() + ggplot2::ggtitle("tSNE") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
legend.position = "none")
umap = Seurat::DimPlot(sobj, group.by = "Seurat.Phase",
reduction = paste0("RNA_pca_", ndims, "_umap")) +
Seurat::NoAxes() + ggplot2::ggtitle("UMAP") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
tsne | umap
We can visualize the cell cycle, from cyclone :
tsne = Seurat::DimPlot(sobj, group.by = "cyclone.Phase",
reduction = paste0("RNA_pca_", ndims, "_tsne")) +
Seurat::NoAxes() + ggplot2::ggtitle("tSNE") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
legend.position = "none")
umap = Seurat::DimPlot(sobj, group.by = "cyclone.Phase",
reduction = paste0("RNA_pca_", ndims, "_umap")) +
Seurat::NoAxes() + ggplot2::ggtitle("UMAP") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
tsne | umap
We visualize the clustering :
tsne = Seurat::DimPlot(sobj, group.by = "seurat_clusters", label = TRUE,
reduction = paste0("RNA_pca_", ndims, "_tsne")) +
Seurat::NoAxes() + ggplot2::ggtitle("tSNE") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
legend.position = "none")
umap = Seurat::DimPlot(sobj, group.by = "seurat_clusters", label = TRUE,
reduction = paste0("RNA_pca_", ndims, "_umap")) +
Seurat::NoAxes() + ggplot2::ggtitle("UMAP") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
tsne | umap
We visualize all cell types markers on the tSNE :
markers = dotplot_markers %>% unlist() %>% unname()
markers = markers[markers %in% rownames(sobj)]
plot_list = lapply(markers,
FUN = function(one_gene) {
p = Seurat::FeaturePlot(sobj, features = one_gene,
reduction = paste0("RNA_pca_", ndims, "_tsne")) +
ggplot2::labs(title = one_gene) +
ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
ggplot2::theme(aspect.ratio = 1,
plot.subtitle = element_text(hjust = 0.5)) +
Seurat::NoAxes()
return(p)
})
patchwork::wrap_plots(plot_list, ncol = 4)
We save the annotated and filtered Seurat object :
saveRDS(sobj, file = paste0(out_dir, "/datasets/", sample_name, "_sobj_filtered.rds"))
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
##
## locale:
## [1] C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.3.5 patchwork_1.1.2 dplyr_1.0.7
##
## loaded via a namespace (and not attached):
## [1] softImpute_1.4 graphlayouts_0.7.0
## [3] pbapply_1.4-2 lattice_0.20-41
## [5] haven_2.3.1 vctrs_0.3.8
## [7] usethis_2.0.1 dynwrap_1.2.1
## [9] blob_1.2.1 survival_3.2-13
## [11] prodlim_2019.11.13 dynutils_1.0.5
## [13] later_1.3.0 DBI_1.1.1
## [15] R.utils_2.11.0 SingleCellExperiment_1.8.0
## [17] rappdirs_0.3.3 uwot_0.1.8
## [19] dqrng_0.2.1 jpeg_0.1-8.1
## [21] zlibbioc_1.32.0 pspline_1.0-18
## [23] pcaMethods_1.78.0 mvtnorm_1.1-1
## [25] htmlwidgets_1.5.4 GlobalOptions_0.1.2
## [27] future_1.22.1 UpSetR_1.4.0
## [29] laeken_0.5.2 leiden_0.3.3
## [31] clustree_0.4.3 parallel_3.6.3
## [33] scater_1.14.6 irlba_2.3.3
## [35] DEoptimR_1.0-9 tidygraph_1.1.2
## [37] Rcpp_1.0.9 readr_2.0.2
## [39] KernSmooth_2.23-17 carrier_0.1.0
## [41] promises_1.1.0 gdata_2.18.0
## [43] DelayedArray_0.12.3 limma_3.42.2
## [45] graph_1.64.0 RcppParallel_5.1.4
## [47] Hmisc_4.4-0 fs_1.5.2
## [49] RSpectra_0.16-0 fastmatch_1.1-0
## [51] ranger_0.12.1 digest_0.6.25
## [53] png_0.1-7 sctransform_0.2.1
## [55] cowplot_1.0.0 DOSE_3.12.0
## [57] ggvenn_0.1.9 here_1.0.1
## [59] TInGa_0.0.0.9000 ggraph_2.0.3
## [61] pkgconfig_2.0.3 GO.db_3.10.0
## [63] DelayedMatrixStats_1.8.0 gower_0.2.1
## [65] ggbeeswarm_0.6.0 iterators_1.0.12
## [67] DropletUtils_1.6.1 reticulate_1.26
## [69] clusterProfiler_3.14.3 SummarizedExperiment_1.16.1
## [71] circlize_0.4.15 beeswarm_0.4.0
## [73] GetoptLong_1.0.5 xfun_0.35
## [75] bslib_0.3.1 zoo_1.8-10
## [77] tidyselect_1.1.0 reshape2_1.4.4
## [79] purrr_0.3.4 ica_1.0-2
## [81] pcaPP_1.9-73 viridisLite_0.3.0
## [83] rtracklayer_1.46.0 rlang_1.0.2
## [85] hexbin_1.28.1 jquerylib_0.1.4
## [87] dyneval_0.9.9 glue_1.4.2
## [89] RColorBrewer_1.1-2 matrixStats_0.56.0
## [91] stringr_1.4.0 lava_1.6.7
## [93] europepmc_0.3 DESeq2_1.26.0
## [95] recipes_0.1.17 labeling_0.3
## [97] httpuv_1.5.2 class_7.3-17
## [99] BiocNeighbors_1.4.2 DO.db_2.9
## [101] annotate_1.64.0 jsonlite_1.7.2
## [103] XVector_0.26.0 bit_4.0.4
## [105] mime_0.9 aquarius_0.1.5
## [107] Rsamtools_2.2.3 gridExtra_2.3
## [109] gplots_3.0.3 stringi_1.4.6
## [111] processx_3.5.2 gsl_2.1-6
## [113] bitops_1.0-6 cli_3.0.1
## [115] batchelor_1.2.4 RSQLite_2.2.0
## [117] randomForest_4.6-14 tidyr_1.1.4
## [119] data.table_1.14.2 rstudioapi_0.13
## [121] org.Mm.eg.db_3.10.0 GenomicAlignments_1.22.1
## [123] nlme_3.1-147 qvalue_2.18.0
## [125] scran_1.14.6 locfit_1.5-9.4
## [127] scDblFinder_1.1.8 listenv_0.8.0
## [129] ggthemes_4.2.4 gridGraphics_0.5-0
## [131] R.oo_1.24.0 dbplyr_1.4.4
## [133] BiocGenerics_0.32.0 TTR_0.24.2
## [135] readxl_1.3.1 lifecycle_1.0.1
## [137] timeDate_3043.102 ggpattern_0.3.1
## [139] munsell_0.5.0 cellranger_1.1.0
## [141] R.methodsS3_1.8.1 proxyC_0.1.5
## [143] visNetwork_2.0.9 caTools_1.18.0
## [145] codetools_0.2-16 Biobase_2.46.0
## [147] GenomeInfoDb_1.22.1 vipor_0.4.5
## [149] lmtest_0.9-38 msigdbr_7.5.1
## [151] htmlTable_1.13.3 triebeard_0.3.0
## [153] lsei_1.2-0 xtable_1.8-4
## [155] ROCR_1.0-7 BiocManager_1.30.10
## [157] scatterplot3d_0.3-41 abind_1.4-5
## [159] farver_2.0.3 parallelly_1.28.1
## [161] RANN_2.6.1 askpass_1.1
## [163] GenomicRanges_1.38.0 RcppAnnoy_0.0.16
## [165] tibble_3.1.5 ggdendro_0.1-20
## [167] cluster_2.1.0 future.apply_1.5.0
## [169] Seurat_3.1.5 dendextend_1.15.1
## [171] Matrix_1.3-2 ellipsis_0.3.2
## [173] prettyunits_1.1.1 lubridate_1.7.9
## [175] ggridges_0.5.2 igraph_1.2.5
## [177] RcppEigen_0.3.3.7.0 fgsea_1.12.0
## [179] remotes_2.4.2 scBFA_1.0.0
## [181] destiny_3.0.1 VIM_6.1.1
## [183] testthat_3.1.0 htmltools_0.5.2
## [185] BiocFileCache_1.10.2 yaml_2.2.1
## [187] utf8_1.1.4 plotly_4.9.2.1
## [189] XML_3.99-0.3 ModelMetrics_1.2.2.2
## [191] e1071_1.7-3 foreign_0.8-76
## [193] withr_2.5.0 fitdistrplus_1.0-14
## [195] BiocParallel_1.20.1 xgboost_1.4.1.1
## [197] bit64_4.0.5 foreach_1.5.0
## [199] robustbase_0.93-9 Biostrings_2.54.0
## [201] GOSemSim_2.13.1 rsvd_1.0.3
## [203] memoise_2.0.0 evaluate_0.18
## [205] forcats_0.5.0 rio_0.5.16
## [207] geneplotter_1.64.0 tzdb_0.1.2
## [209] caret_6.0-86 ps_1.6.0
## [211] DiagrammeR_1.0.6.1 curl_4.3
## [213] fdrtool_1.2.15 fansi_0.4.1
## [215] highr_0.8 urltools_1.7.3
## [217] xts_0.12.1 GSEABase_1.48.0
## [219] acepack_1.4.1 edgeR_3.28.1
## [221] checkmate_2.0.0 scds_1.2.0
## [223] cachem_1.0.6 npsurv_0.4-0
## [225] babelgene_22.3 rjson_0.2.20
## [227] openxlsx_4.1.5 ggrepel_0.9.1
## [229] clue_0.3-60 rprojroot_2.0.2
## [231] stabledist_0.7-1 tools_3.6.3
## [233] sass_0.4.0 nichenetr_1.1.1
## [235] magrittr_2.0.1 RCurl_1.98-1.2
## [237] proxy_0.4-24 car_3.0-11
## [239] ape_5.3 ggplotify_0.0.5
## [241] xml2_1.3.2 httr_1.4.2
## [243] assertthat_0.2.1 rmarkdown_2.18
## [245] boot_1.3-25 globals_0.14.0
## [247] R6_2.4.1 Rhdf5lib_1.8.0
## [249] nnet_7.3-14 RcppHNSW_0.2.0
## [251] progress_1.2.2 genefilter_1.68.0
## [253] statmod_1.4.34 gtools_3.8.2
## [255] shape_1.4.6 HDF5Array_1.14.4
## [257] BiocSingular_1.2.2 rhdf5_2.30.1
## [259] splines_3.6.3 AUCell_1.8.0
## [261] carData_3.0-4 colorspace_1.4-1
## [263] generics_0.1.0 stats4_3.6.3
## [265] base64enc_0.1-3 dynfeature_1.0.0
## [267] smoother_1.1 gridtext_0.1.1
## [269] pillar_1.6.3 tweenr_1.0.1
## [271] sp_1.4-1 ggplot.multistats_1.0.0
## [273] rvcheck_0.1.8 GenomeInfoDbData_1.2.2
## [275] plyr_1.8.6 gtable_0.3.0
## [277] zip_2.2.0 knitr_1.41
## [279] ComplexHeatmap_2.14.0 latticeExtra_0.6-29
## [281] biomaRt_2.42.1 IRanges_2.20.2
## [283] fastmap_1.1.0 ADGofTest_0.3
## [285] copula_1.0-0 doParallel_1.0.15
## [287] AnnotationDbi_1.48.0 vcd_1.4-8
## [289] babelwhale_1.0.1 openssl_1.4.1
## [291] scales_1.1.1 backports_1.2.1
## [293] S4Vectors_0.24.4 ipred_0.9-12
## [295] enrichplot_1.6.1 hms_1.1.1
## [297] ggforce_0.3.1 Rtsne_0.15
## [299] shiny_1.7.1 numDeriv_2016.8-1.1
## [301] polyclip_1.10-0 grid_3.6.3
## [303] lazyeval_0.2.2 Formula_1.2-3
## [305] tsne_0.1-3 crayon_1.3.4
## [307] MASS_7.3-54 pROC_1.16.2
## [309] viridis_0.5.1 dynparam_1.0.0
## [311] rpart_4.1-15 zinbwave_1.8.0
## [313] compiler_3.6.3 ggtext_0.1.0